27 citations · 47 across the 15 of their papers we have counts for
15 papers
When Spike Sparsity Does Not Translate to Deployed Cost: VS-WNO on Jetson Orin Nano
Jason Yoo, Shailesh Garg, Souvik Chakraborty +1
Spiking neural operators are appealing for neuromorphic edge computing because event-driven substrates can, in principle, translate sparse activity into lower latency and energy. W…
CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis
Shailesh Garg, Souvik Chakraborty
Time-dependent reliability analysis of nonlinear dynamical systems under stochastic excitations is a critical yet computationally demanding task. Conventional approaches, such as M…
SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications
Shailesh Garg, Luis Mandl, Somdatta Goswami +1
Energy efficiency remains a critical challenge in deploying physics-informed operator learning models for computational mechanics and scientific computing, particularly in power-co…
Event-driven physics-informed operator learning for reliability analysis
Shailesh Garg, Souvik Chakraborty
Reliability analysis of engineering systems under uncertainty poses significant computational challenges, particularly for problems involving high-dimensional stochastic inputs, no…
NeuroPINNs: Neuroscience Inspired Physics Informed Neural Networks
Shailesh Garg, Souvik Chakraborty
We introduce NeuroPINNs, a neuroscience-inspired extension of Physics-Informed Neural Networks (PINNs) that incorporates biologically motivated spiking neuron models to achieve ene…
Distribution-Free Uncertainty-Aware Virtual Sensing via Conformalized Neural Operators
Kazuma Kobayashi, Shailesh Garg, Farid Ahmed +2
Robust uncertainty quantification (UQ) remains a critical barrier to the safe deployment of deep learning in real-time virtual sensing, particularly in high-stakes domains where sp…
Hybrid variable spiking graph neural networks for energy-efficient scientific machine learning
Isha Jain, Shailesh Garg, Shaurya Shriyam +1
Graph-based representations for samples of computational mechanics-related datasets can prove instrumental when dealing with problems like irregular domains or molecular structures…
Distribution free uncertainty quantification in neuroscience-inspired deep operators
Shailesh Garg, Souvik Chakraborty
Energy-efficient deep learning algorithms are essential for a sustainable future and feasible edge computing setups. Spiking neural networks (SNNs), inspired from neuroscience, are…
Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator
Shailesh Garg, Souvik Chakraborty
We propose, in this paper, a Variable Spiking Wavelet Neural Operator (VS-WNO), which aims to bridge the gap between theoretical and practical implementation of Artificial Intellig…
Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression
Shailesh Garg, Souvik Chakraborty
Redundant information transfer in a neural network can increase the complexity of the deep learning model, thus increasing its power consumption. We introduce in this paper a novel…
Randomized prior wavelet neural operator for uncertainty quantification
Shailesh Garg, Souvik Chakraborty
In this paper, we propose a novel data-driven operator learning framework referred to as the \textit{Randomized Prior Wavelet Neural Operator} (RP-WNO). The proposed RP-WNO is an e…
Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations
Shailesh Garg, Souvik Chakraborty
Neural network based data-driven operator learning schemes have shown tremendous potential in computational mechanics. DeepONet is one such neural network architecture which has ga…
Assessment of DeepONet for reliability analysis of stochastic nonlinear dynamical systems
Shailesh Garg, Harshit Gupta, Souvik Chakraborty
Time dependent reliability analysis and uncertainty quantification of structural system subjected to stochastic forcing function is a challenging endeavour as it necessitates consi…
Physics-integrated hybrid framework for model form error identification in nonlinear dynamical systems
Shailesh Garg, Souvik Chakraborty, Budhaditya Hazra
For real-life nonlinear systems, the exact form of nonlinearity is often not known and the known governing equations are often based on certain assumptions and approximations. Such…
Machine learning based digital twin for stochastic nonlinear multi-degree of freedom dynamical system
Shailesh Garg, Ankush Gogoi, Souvik Chakraborty +1
The potential of digital twin technology is immense, specifically in the infrastructure, aerospace, and automotive sector. However, practical implementation of this technology is n…